FRESH INSIGHTS FROM REAL-TIME DATA, COURTESY AI-DRIVEN ANALYTICS

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1 FRESH INSIGHTS FROM REAL-TIME DATA, COURTESY AI-DRIVEN ANALYTICS Do customers care about real vanilla versus artificial flavors? Does high quality trump premium pricing? Confectionary brands try to answer countless such questions as they assess customer preferences to make product line decisions. Digital has thrust large enterprises in super-fast freeway tunnels where traditional mechanisms of customer surveys do not work. Enterprises thus reckon that real-time data analytics is the way to go. They collect an enormous amount of data from every business touch point, hoping to process it to understand grassroots dynamics for critical decision making. But that s where the real challenge begins.

2 DATA DELUGE As per a study by Greyhound Research, a leading global analyst firm, enterprises realize that it s not just about collecting data and creating large data pools, but also having the ability to make sense of this data deluge. This reflects in shifting priorities towards leveraging digital technologies, such as data analytics, machine learning, and Artificial Intelligence (AI). 93% respondents of a study cited AI-based analytics as an essential prerequisite for the delivery of great customer experience. Still, only 32% confirmed that they either already have such a system in place or are in the process of establishing it. One of the world s leading confectionery brands, an Infosys client, was struggling with the vast amount of data they generated - about 1 TB a month. They were stuck in a situation where the data was spread across multiple systems that were not in sync with one another, resulting in inaccurate analysis and inability to generate real-time analytics. Another challenge was the limited capacity of on-premise infrastructure that made it difficult to execute complex analytical computations. This hurt the ability of their data scientists to generate meaningful insights for the client s marketing and supply chain. The brand needed a platform that could scale on demand, and seamlessly handle growing volumes of data. Infosys partnered with the brand to create a scalable data analytics platform based on Infosys Nia, an AI platform, and a unified data lake.

3 NEAR REAL-TIME ANALYTICS The team also created a unified data lake, leveraging Nia for data ingestion from multiple sources. Nia automated the entire process of ingesting big data and creating meaningful models for analytics. The platform, with its plug-and-play format, required little support. Infosys also integrated Nia with analytical tools such as R Studio and Shiny Server, to create actionable information for the client s data scientists. With this solution, the brand s data scientists now had near real-time data to produce insights, instead of a daily snapshot. A well-devised strategy enabled the client to accomplish near real-time analytics on large and heterogeneous sets of data. The platform was flexible, fast, and scalable for on-demand expansion. Not only did it speed up time to market for analytics, but Infosys also drastically reduced the operating costs of the client s data infrastructure by providing the AI platform on a cost-efficient Platform-As-A-Service (PaaS) model.

4 FRESH INSIGHTS FROM REAL-TIME DATA, COURTESY AI-DRIVEN ANALYTICS: THE FIVE TAKEAWAYS 1 Build data lake to create a single reference point for data sets that can be leveraged for all analytics requirements. 2 Implement an AI platform for establishing a scalable data analytics solution for data ingestion from multiple varied sources and processing data into well-defined data models. 3 Look for opportunities to automate various stages of data processing for analytics ingestion, processing, monitoring for reliable infrastructure operations. 4 Integrate the AI platform with other analytics tools to generate actionable information for critical decision making, in real-time. 5 Deploy the AI platform on a Platform-as-a-Service (PaaS) model for scalability and cost-effectiveness.

5 BIG LEARNING: Data deluge poses a considerable challenge for large enterprises trying to rev up their marketing engine. While there s tremendous opportunity to create intelligent insights from these large sets of data through AI, implementing a functional, consistent, reliable, and scalable AI platform is not possible without a welldevised and carefully executed strategy. It is, hence, critical to involve experts and leverage the experience of large-scale data analytics and AI roll-outs. WE DID THIS FOR THEM. WE CAN DO IT FOR YOU. Learn more about generating real-time data analytics by reaching out to us at askus@infosys.com 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosysacknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.